Project Introduction Grades often serve as a proxy for academic performance, but they can also reflect structural trends, major events, or departmental norms. In this project, I explore GPA trends across five popular majors at the University of Virginia—Economics, Statistics, Computer Science, Psychology, and Biology. My motivation stems from growing discourse on grade inflation, equity, and the long-term effects of events like COVID-19 on academic standards.

As a student majoring in both Statistics and Economics, I wanted to investigate whether perceptions of easier grading in some departments hold up in the data and whether certain types of courses (e.g., upper-level electives) are systematically more generous in their grading.

Background Information The dataset includes GPA information from Spring 2020 to Fall 2024, sourced from UVA’s publicly available Institutional Research & Analytics site. I used course-level GPA data, number of students, instructors, and term information to study grade distributions, changes over time, and differences between lower- and upper-level classes.

I also contextualized the trends with major academic disruptions:

Spring 2020 - Spring 2021: abrupt shift to online learning and default Credit/No Credit (CR/NC) due to COVID-19

Fall 2022: another CR/NC option granted after the tragic on-campus shooting in November 2022

Visualization 1

This interactive line chart tracks the average GPA at UVA from Spring 2020 through Spring 2024, with shaded bands marking two major institutional events: the COVID-19 grading policy shift and the Fall 2022 campus shooting. During the COVID period, students had the option to take classes as Credit/No Credit, likely contributing to elevated GPA averages, particularly in Spring 2021.

Interestingly, GPA also spikes in Spring 2022, even though no formal grading leniency was in place—potentially reflecting lasting effects of online learning or departmental carryover in grading culture. Following Fall 2022, average GPA trends downward, suggesting a return to more standardized grading.

This plot provides crucial temporal context for understanding department-level trends and shows how both policy and broader academic disruptions can shape aggregate academic performance.

Visualization 2 This line chart tracks average GPA trends from Spring 2020 to Fall 2024 across five majors: Biology, Computer Science, Economics, Psychology, and Statistics. Early semesters show GPA spikes across most departments, particularly during Spring 2021 when COVID-era grading accommodations were in full effect. STAT and CS had the highest GPAs in this period, both nearing or exceeding 3.8.

After the temporary grading leniency ended, GPA levels dropped sharply in Fall 2021, especially in Economics and CS. Over time, departments like STAT and PSYC show relatively stable or rebounding GPA patterns, while Economics consistently trends lower, suggesting tougher grading or course difficulty.

This visualization reveals important differences in grading behavior between departments and helps frame later analyses that separate upper-level and introductory course patterns.

Visualization 3

This violin plot visualizes GPA distributions in key introductory courses across five majors, including Econ 2010, CS 1110, Stat 2020, Biol 2100, and Psyc 1010. These courses are often taken either to fulfill general education requirements or as exploratory steps toward declaring a major, making their grading patterns especially impactful on student decision-making.

We observe notable variation in grade distributions across departments. For example, CS 1110 and Stat 2020 exhibit relatively high medians and narrow spreads, suggesting more grade clustering, whereas courses like Biol 2100 and Econ 2020 display wider ranges with lower median GPAs. Psyc 2600 shows a slight right skew, possibly reflecting more grade inflation or lenient grading norms.

This visualization raises questions about accessibility and perceived rigor, especially for students early in their academic journey. Departments with harsher grading in intro courses may unintentionally discourage students from pursuing the major, even if upper-level classes offer more support or different assessment structures.

Visualization 4

This alluvial plot visualizes GPA distributions across several high-impact core courses that are often taken before students officially declare their major. These courses—including Econ 3010, Stat 3110, CS 2130, and Biol 3000—are commonly seen as gatekeepers or “weed-out” classes, with performance in them often influencing whether students continue in the major. For context, CS 2150 was an earlier version of CS 2130, phased out as the curriculum evolved over the last five years.

The plot shows that in most of these courses, the largest share of students fall into the B-range GPA bucket, with only a small fraction achieving A-range GPAs. C-range grades are also prevalent in certain courses like CS 2130 and Econ 3010, underscoring their difficulty and potential filtering effect.

This visual highlights how early performance in these classes could reinforce disparities in major continuation rates, particularly for students with less academic preparation or external responsibilities. It also reveals variation in grading severity across departments even in foundational courses.

Visualization 5

This animated bubble chart tracks average GPA awarded by instructors across key major courses over the past five years. Each bubble represents a course taught by a specific instructor during a given semester. The x-axis reflects the average GPA given, the y-axis lists the course, and the color identifies the instructor. Crucially, the size of each bubble corresponds to the number of students enrolled in that instructor’s section.

The animation shows that some instructors consistently grade higher or lower than their peers for the same course, and that large-enrollment sections often hover near the mid-GPA range—suggesting a degree of standardization in larger lectures. Smaller bubbles on the higher or lower ends may represent outlier grading practices or unique course versions.

This visualization offers a more granular look at grading disparities and makes it easier to identify whether GPA differences stem from departmental norms or individual instructor tendencies.

Visualization 6

This hexbin plot directly visualizes the relationship between class size and average GPA in six core courses across the selected majors. Unlike Visualization 5—where class size was implied by bubble size—this plot makes it explicit through the x-axis (class size) and a color gradient that represents the total number of students across sections, ranging from deep purple (fewer students) to bright yellow (many students).

Several interesting patterns emerge: larger classes like BIOL 3000 and ECON 3010 tend to have GPAs clustered in the 2.8–3.4 range, suggesting a grading norm or ceiling in high-enrollment courses. Meanwhile, CS and STAT classes show more variability, with smaller sections sometimes yielding higher average grades.

This visualization provides compelling evidence that course size may be inversely related to GPA, especially in departments with tightly standardized grading policies. It also underscores the importance of separating large lectures from smaller seminars when evaluating grade distribution fairness or rigor.

Visualization 7

This ridgeline plot displays the distribution of the percentage of students earning a B+ or higher in 4000-level (upper-level) courses across five departments. Each department’s curve shows where their upper-level classes tend to fall in terms of grade outcomes, providing a sense of grading generosity at the advanced level.

STAT and PSYC departments appear to offer the most consistent access to high grades, with many of their 4000-level courses seeing over 90% of students earning at least a B+. In contrast, CS and ECON show more variability and lower central tendencies, indicating that high marks are less guaranteed in those upper-level offerings.

Interestingly, BIOL has the sharpest rightward peak, with many courses concentrated near or at 100%, which may suggest grade inflation or a shift toward leniency in advanced biology classes. This visualization helps quantify how “attainable” top grades are once students progress to advanced coursework, revealing both inter-departmental differences and internal grading cultures.

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Visualization 8

To examine how different course characteristics relate to GPA outcomes, this pairwise plot visualizes four key variables across undergraduate courses in five major departments (STAT, PSYC, ECON, CS, BIOL): average course GPA, class size, the share of students earning a B+ or higher, and the number of students receiving D, F, or withdrawal grades (DFWs). Each lower-panel scatterplot is color-coded by department, while the diagonal panels show kernel density estimates of each variable’s distribution.

The upper triangle shows both the overall Pearson correlation and department-specific values for each variable pairing. For instance, there is a strong positive correlation (0.95) between average GPA and B+ share, which is consistent across departments. Meanwhile, GPA and DFWs are negatively correlated overall (-0.57), with even stronger relationships in CS (-0.72) and BIOL (-0.68), suggesting that as GPA rises, DFW rates drop more steeply in these majors. Class size shows a mild negative relationship with GPA (-0.28), though it’s most pronounced in BIOL (-0.53), where larger classes tend to correlate with lower performance.

This visualization brings together multiple variables in a compact format, revealing meaningful patterns in course difficulty, grading distributions, and departmental trends. It reinforces the idea that performance metrics in courses are not only influenced by course size but also vary by subject area, with CS and BIOL showing more extreme relationships than STAT or PSYC.

Visualization 9

This visualization highlights the courses with the highest and lowest proportions of students receiving a particular grade category—A (A+, A, A−), B (B+, B, B−), C (C+, C, C−), or D/F/W (Withdrawals included). By converting raw grade data into share percentages, this tool enables clear, semester-by-semester comparisons across departments and course levels.

The visualization serves both students and advisors by revealing which classes historically award high proportions of top or low grades. Users can interactively filter by semester, department, course level, and grade category to explore grade trends and identify consistently high- or low-grading courses.

While some courses fluctuate in their ranking from semester to semester—likely due to changes in instructors, class size, or policy—others remain reliably at the top or bottom. These consistently ranked classes may become prime candidates for students to prioritize or avoid when enrolling, depending on their academic goals and risk tolerance.

Access the Shiny App Here: https://krishnambhamidipati.shinyapps.io/STAT3280ProjectShinyApp1/

Visualization 10

This Shiny app explores how the distribution of grades varies across different majors, course levels, and class sizes. By breaking down grade shares into A, B, C, and D/F/W categories, students can get a granular view of grade distributions at UVA from Fall 2020 to Fall 2024.

Each grade share is calculated as the percentage of students earning a particular grade band (e.g., A+ to A– for A share) within a course. The faceted scatterplots display these shares against class size, offering insight into how class size might relate to grade outcomes by department. For instance, smaller classes in some majors may have higher A shares, while others do not show a strong correlation.

In addition, density plots help compare the overall distribution of grade shares by major. These plots reveal interesting patterns—such as the heavy concentration of high A shares in Psychology and Statistics, or the broader spread of B shares in Economics and CS. Meanwhile, DFW shares remain low for most majors but are visibly higher in select CS and ECON courses.

These tools give students an interactive way to identify grading trends within and across departments. Whether planning their academic schedules or simply curious about grading patterns, users can isolate variables like course level and class size to explore where high (or low) grade shares are most common.

Access the Shiny App Here: https://krishnambhamidipati.shinyapps.io/STAT3280ShinyApp2/

Conclusion This project offers a comprehensive exploration of GPA trends at UVA from multiple angles—over time, across departments, and by course type and structure. Through static and interactive visualizations, we observe clear evidence of grading shifts tied to institutional events like COVID-19 and the 2022 campus shooting, as well as longer-term departmental grading norms. The data suggests that majors like STAT and PSYC consistently offer more favorable grade distributions, while ECON and CS tend to grade more rigorously, especially in early and high-enrollment courses.

Beyond identifying these patterns, the project raises important questions about fairness, academic culture, and student decision-making. Introductory “weed-out” classes and inconsistent grading across instructors can have lasting effects on student trajectories, potentially deterring capable students from pursuing certain fields. By making these trends more transparent, this analysis can help inform ongoing discussions about grade inflation, academic equity, and the true meaning of performance in higher education.